AI-driven approaches for air pollution modelling: A comprehensive systematic review
Lorenzo Garbagna, Lakshmi Babu Saheer, Mahdi Maktabdar Oghaz
Abstract
In recent years, air quality levels have become a global issue with the rise of harmful pollutants and their effects on climate change. Urban areas are especially affected by air pollution, resulting in a deterioration of the environment and a surge in health complications. Research has been conducted on different studies that accurately predict future pollution concentration levels utilising different methods. This paper introduces the current physical models for air quality prediction and conducts an extensive systematic literature review on Machine Learning and Deep Learning techniques for predicting pollutants. This work compares different methodologies and techniques by grouping studies that utilise similar approaches together and comparing them. Furthermore, a distinction is made between temporal and spatiotemporal models to understand and highlight how both approaches impact future air pollutant concentration level predictions. The review differs from similar works as it focuses not only on comparing models and approaches but by analysing how the usage of external features, such as meteorological data, traffic information, and land usage, affect pollutant levels and the model’s accuracy on air quality forecasting. Performances and limitations are explored for both Machine and Deep Learning approaches, and the work offers a discussion on their comparison and possible future developments in this research space. This review highlights how Deep Learning models tend to be more suitable for forecasting problems due to their feature and spatio-temporal correlation representation abilities, as well as providing different directions for further work, from models utilisation to feature inclusion. • A systematic review of Machine and Deep Learning techniques for air quality forecasting. • Comparison between models and external feature utilisation for each approach. • Performance and limitations discussion for both Machine and Deep Learning methodologies. • Suggestions for further work and research direction based on the review analysis.